Accuracy of the Preferred Language Field in the Electronic Health Records of Two Canadian Hospitals

Abstract

Background The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed. Objectives This study aimed to audit the accuracy of EHR language data at two academic hospitals in Toronto, Ontario, Canada. Methods The EHR language was compared with a patient's stated preferred language by interview. Language was dichotomized to English or non-English. Agreement between language documented in the EHR and patient-reported preferred language was calculated using sensitivity, specificity, and positive predictive value (PPV). Results A total of 323 patients were interviewed, including 96 with a stated non-English preferred language. The sensitivity of the EHR for English-language preference was high at both hospitals: 100% at hospital A with a PPV of 88%, and 99% at hospital B with a PPV of 85%. However, the sensitivity of the EHR for non-English preference differed greatly between the two hospitals. The sensitivity was 81% with a PPV of 100% at hospital A and the sensitivity was 12% with a PPV of 60% at hospital B. Conclusion The accuracy of the EHR for identifying non-English language preference differed greatly between the hospitals studied. Language data must be accurate for it to be used, and regular quality assurance is required.

Association between non‐English language and use of physical and chemical restraints among medical inpatients with delirium

“…After excluding unavailable records, 997 patients' charts were screened to identify those with delirium using a validated abstraction tool 2 . Self‐reported preferred language is a previously validated variable that came from hospital medical records 3 . Patient demographics, comorbidities, Laboratory‐based Acute Physiology Score (LAPS), 4 physical restraint use, and physician orders for new antipsychotic or sedative‐hypnotic medications were collected from hospital medical records and administrative databases (definitions in Text S1).…”

“…2 Self-reported preferred language is a previously validated variable that came from hospital medical records. 3 Patient demographics, comorbidities, Laboratory-based Acute Physiology Score (LAPS), 4 physical restraint use, and physician orders for new antipsychotic or sedative-hypnotic medications were collected from hospital medical records and administrative databases (definitions in Text S1). We could not confirm if medication orders were administered but confirmed the application of physical restraints by reviewing nursing documentation.…”

Achieving Greater Health Equity: An Opportunity for Anesthesiology

“…There are many efforts underway leveraging clinical informatics and data science to increase comprehensive and accurate collection of vital demographic data in EHRs and large-scale data registries. 8 Initiatives include: improving the administrative process to collect demographic data; allowing patients to self-record their race and ethnicity information through patient portals; validating the accuracy of entered data 78 using machine learning and natural language processing approaches to fill in demographic data from clinical notes with free text 73 ; and cross-linking of datasets to validate and impute missing demographic data on patients in an EHR. 79 Significantly increasing the scope and accuracy of data collection of race and ethnicity data is one of the strongest foundational needs to address and advance health equity in anesthesiology.…”

Recognizing and Dismantling Raciolinguistic Hierarchies in Latinx Health

“…Ensure electronic health record systems have user-friendly fields to facilitate accurate language documentation. 27…”

“…Such incorrect labeling may result in underrecognizing the need for onsite professional medical interpreters-an evidence-based intervention that significantly improves communication, patient outcomes, patient satisfaction, and health care utilization. 32 To dismantle raciolinguistic hierarchies in Latinx patient care, health care centers must ensure that staff are trained in clear policies and procedures regarding accurate, consistent, and respectful collection of demographic information, including language preference, 2,27 and that patients and staff can easily access professional language services 28 (see Table ).…”